Monitoring device for management of insulin delivery

• US 8,954,373 B2
• Filed: 03/29/2012
• Issued: 02/10/2015
• Est. Priority Date: 09/30/2009
• Status: Active Grant

• Current Assignee: Dreamed Diabetes Ltd.
• Original Assignee: Dreamed Diabetes Ltd.
• Inventors: Atlas, Eran, Nimri, Revital, Grunberg, Eli Aviram, Phillip, Moshe, Miller, Shahar
• Primary Examiner(s): Starks, Wilbert L
• Assistant Examiner(s): Zidanic, Michael
• Application Number:
  US13/434,326
• Publication Number:
  US 20120246106A1
• Time in Patent Office: 1,048 Days
• Field of Search: 706/52
• US Class Current: 706/52
• CPC Class Codes: A61B 5/14532 for measuring glucose, e.g. by tissue impedance measurement
• CPC Class Codes: A61M 2005/14208 with a programmable infusion control system, characterised by the infusion program
• CPC Class Codes: A61M 2230/201 Glucose concentration
• CPC Class Codes: A61M 5/1723 using feedback of body parameters, e.g. blood-sugar, pressure measurement of body parameters A61B5/00
• CPC Class Codes: G06N 7/00 Computing arrangements based on specific mathematical models
• CPC Class Codes: G16H 20/17 delivered via infusion or injection
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems

Abstract

Monitoring system and method for use with diabetic treatment management. The system includes: a communication interface configured to permit access to stored raw log data, obtained over a certain time, being indicative of glucose measurements, meals consumed and insulin delivery; and a control unit including an unsupervised learning controller configured to receive and process said raw log data and determine at least one global insulin pump setting of basal rate, correction factor, carbohydrate ratio and insulin activity curve parameters. The system may include a processing unit including a first processor for processing measured data indicative of blood glucose level and generating first processed data, a second processor including at least one fuzzy logic module which receives input parameters corresponding to the measured data, the first processed data and a reference data, and processes the data to produce a qualitative output parameter to determine whether any treatment parameter should be modified.

Claims

1. A monitoring system for use with diabetic treatment management, the monitoring system comprising:

a communication interface configured and operable to permit access to stored raw log data obtained over a certain time and being time spaced data points of glucose measurements, meals consumed and insulin delivery;

a control unit configured for receiving and processing said raw log data, the control unit comprising;

a sectioning module configured to section the raw log data within at least one time window;

the sectioned time window being at least one of Basal data Section (BaS), Meals data Section (MS) and Bolus data Section (BS), each sectioned time window having a starting point and having an end point such that the BaS being selected outside an effect window of either a meal event or an insulin bolus, the MS being selected at a predetermined time ahead of a meal data point, and the starting point of the BS being selected as one of the following;

(a) the end point of the MS or the BaS, and (b) an insulin bolus data point which is outside the MS;

the end point of the BS being selected as one of the following, (i) the starting point of the MS or BaS and (ii) a predetermined time ahead of the insulin bolus data point without any intervening insulin bolus; and

an unsupervised learning controller configured and operable to determine an informative data piece from a residual log data portion of said raw log data, analyze said informative data piece and select a sectioned time window for calculation of an individualized patient'"'"'s profile related data comprising at least one of global insulin pump setting of basal rate, correction factor (CF), carbohydrate ratio (CR) and insulin activity curve parameters, wherein calculation of the basal rate is based on BaS, calculation of at least one of insulin activity curve parameters, correction factor (CF) and carbohydrate ratio (CR) is based on MS, and calculation of the correction factor (CF) or insulin activity curve parameters is based on BS.

2. The system of claim 1, wherein said raw log data is acquired in accordance with a preprogrammed sampling pattern.

3. The system of claim 1, wherein said unsupervised learning controller is configured and operable determine each of said parameters from a part of said informative data piece corresponding to a selected time window of said certain time.

4. The system of claim 1, wherein the received raw log data corresponds to a memory image at the access time irrespective of any user interaction.

5. The system of claim 1, wherein said control unit comprises a controller associated with said communication interface and preprogrammed for receiving said data according to a predetermined sampling time pattern.

6. The system of claim 1, wherein the received stored data corresponds to a memory image at the access time irrespective of any user interaction.

7. The system of claim 1, comprising a memory module configured and operable to maintain said stored data.

8. The system of claim 1, wherein the stored data is being obtained from a remote controller.

9. The system of claim 8, wherein the stored data is accessible via asynchronous operation independent of a user operation.

10. The system of claim 1, wherein the stored data is a memory image of a remote controller independently accumulating said information.

11. The system of claim 1, wherein the stored data is being obtained from a remote controller independently accumulating said information over time.

12. The system of claim 1, wherein the information indicative glucose sensor readings, insulin delivery and meals recordation is a file obtained from a remote controller independently accumulating said information.

13. The system of claim 1, wherein the stored data decoded in a file.

14. The system of claim 12, wherein the file is downloaded from a network and stored in a memory module.

37. The monitoring system of claim 1, further comprising a processing unit comprising:

a first processor module for processing measured data indicative of blood glucose level and generating first processed data indicative thereof,

a second processor module comprising at least one fuzzy logic module;

said fuzzy logic module receives input parameters corresponding to the measured data, the first processed data and a reference data including said individualized patient'"'"'s profile related data, individualized patient'"'"'s treatment history related data, processes the received parameters to produce at least one qualitative output parameter indicative of patient'"'"'s treatment parameters;

such that said second processor module determines whether any of the treatment parameters is to be modified and generate corresponding output data which can be supplied directly to the pump and/or presented through a user interface to an authorized person (patient and/or physician) for a decision making and/or recording.

38. The system of claim 37, wherein said input parameters include at least one of the following input parameters:

past blood glucose level trend, current blood glucose level, future blood glucose level trend, future blood glucose level.

39. The system of claim 37, wherein said at least one fuzzy logic module is characterized by at least one of the following:

(i) it comprises a set of rules associated with contribution factors and at least one fuzzy engine utilizing one or more member functions modeled for translating the input parameters into at least one qualitative output parameter; and

(ii) is configured and operable to provide the at least one output parameter comprising data indicative of at least one of bolus glucagon, bolus insulin and basal insulin treatment, said second processor module thereby providing control to range output treatment suggestion based on the output parameter of the fuzzy logic module.

40. The system of claim 37, wherein said processing unit comprises a third processor module receiving said at least one qualitative output parameter of the fuzzy logic module and said input parameters corresponding to the measured data, the first processed data and the reference data, and processing said at least one output parameter said input parameters to determine whether any of the treatment parameters is to be modified and generate corresponding output data which can be supplied directly to the pump and/or presented through the user interface to an authorized person (patient and/or physician) for a decision making and/or recording, said treatment parameters comprising at least one of dosing of insulin and glucagon to be delivered.

41. The system of claim 40, wherein the at least one output parameter of the at least one fuzzy logic module comprises data indicative of at least one of bolus glucagon, bolus insulin and basal insulin treatment, and said second processor module thereby provides control to range output treatment suggestion based on the output parameter of the fuzzy logic module, the third processor receiving the control to range output treatment suggestion, and determining said amount in accordance with at least one of a glucose target of the patient'"'"'s profile, patient'"'"'s insulin or glucagon pharmacodynamics, and said measured data.

42. The system of claim 37, wherein said processing unit is operable to update and/or calibrate said individualized patient'"'"'s profile related data during treatment or during monitoring procedure.

43. The system of claim 37, wherein said individualized patient'"'"'s profile related data comprises parameters selected from at least one of global pump settings, insulin sensitivity, glucagon sensitivity, basal plan, insulin/glucagon pharmacokinetics associated data, glucose target level or target range level, and insulin/glucagon activity model.

44. The system of claim 37, wherein said individualized patient'"'"'s treatment history related data comprises patient'"'"'s insulin delivery regimen given to the patient at different hours of the day.

45. The system of claim 37, wherein said second processor module comprises a fuzzy logic module operable in response to an event being invoked by a detector module analyzing at least one pattern of glucose levels indicative of at least one event, said event comprising at least one of sleep, meal, exercise and disease event or rest.

46. The system of claim 45, wherein said system is configured and operable to alternate between at least two fuzzy logic modules, each handling a different event.

47. The system of claim 46, wherein said second processor module is operable as a meal treatment module and is configured to monitor the blood glucose level.

48. The system of claim 47, wherein said input parameters further include at least one of the following input parameters:

time elapsed between detected special events, blood glucose level with respect to said special event.

49. The system of claim 37, wherein said measured data is obtained at a certain time, said measured data comprising at least one of current and past glucose levels relative to said certain time.

15. A method for use in determination of insulin pump settings, the method comprising:

performing unsupervised learning of the insulin pump settings, said unsupervised learning comprising;

a) obtaining raw log data input accumulated on one or more glucose monitoring units recording glucose levels of a single treated patient along a certain time;

b) sectioning the raw log data to predetermined data sections;

each of the predetermined data sections being at least one of Basal data Section (BaS), Meals data Section (MS) and Bolus data Section (BS), each section having a starting point and having an end point such that the BaS being selected outside an effect window of either a meal event or an insulin bolus, the MS being selected at a predetermined time ahead of a meal data point, and the starting point of the BS being selected as one of the following;

(a) the end point of the MS or the BaS, and (b) an insulin bolus data point which is outside the MS;

the end point of the BS being selected as one of the following, (i) the starting point of the MS or BaS and (ii) a predetermined time ahead of the insulin bolus data point without any intervening insulin bolus;

c) determining informative data piece from raw log data input being sectioned to data sections, the informative data piece being determined from said data section; and

d) calculating global insulin pump settings from the informative data piece, wherein said settings include at least one parameter of basal plan, Carbohydrate Ratio (CR), Correction Factor (CF) or Active Insulin Function (AIF) wherein calculation of the basal rate is based on BaS, calculation of at least one of insulin activity curve parameters, correction factor (CF) and carbohydrate ratio (CR) is based on MS, and calculation of the correction factor (CF) or insulin activity curve parameters is based on BS.

16. The method of claim 15 further includes aligning plurality of data portions of said raw log data input along a shared time axis.

17. The method of claim 15 wherein data portions are basal periods.

18. The method of claim 16, including determining a representative data point comprising both a value of aggregated blood glucose levels and a time stamp;

the representative data point is paired to a selected basal period;

the representative data point indicates a basal rate determination for the selected basal period.

19. The method of claim 15 wherein the raw log data input of said Basal Section (Bas) includes a series of basal rates as a function of time;

the method comprising;

determining a time delay characterizing the treated patient at said Basal Section (Bas), said time delay being between a basal treatment rate and changes in the glucose level;

obtaining a plurality of selected basal rates at a delivery time, a respective paired glucose level being at the time delay measured from the delivery time;

determining a resultant basal rate from the plurality of selected basal rates which minimizes a change in the glucose level.

20. The method of claim 15, comprising determining an Active Insulin Function (AIF) by carrying out the following method:

a) obtaining a set of glucose measurements and paired time stamps for the raw log data in the time section;

b) normalizing each glucose measurement of the set thereby obtaining a series of normalized glucose measurements and paired time stamp;

c) Processing said normalized glucose measurements and paired time stamp into a substantially monotonic non-increasing series;

thereby obtaining the Active Insulin Function (AIF).

21. The method of claim 15, wherein carbohydrate ratio (CR) is determined from raw log data input from MS Sections.

22. The method of claim 21 wherein the carbohydrate ratio (CR) is calculated from practical carbohydrate ratios (pracCR) defined as ratio of carbohydrate consumed to delivered insulin for an MS Section.

23. The method of claim 21, including determining plurality of glucose level and paired practical carbohydrate ratios for the MS Section;

the paired practical carbohydrate ratios being a candidate carbohydrate ratios defining a curve.

24. The method of claim 22, wherein the carbohydrate ratio (CR) setting is determined from the candidate practical carbohydrate ratios.

25. The method of claim 22, wherein the carbohydrate ratio (CR) for the meal is calculated by calculating a just-in-time AIF setting to estimate the active insulin in the MS Section.

26. The method of claim 23, wherein carbohydrate ratio (CR) setting is determined by selecting a function which fits the curve of the paired practical carbohydrate ratios.

27. The method of claim 23, wherein carbohydrate ratio (CR) setting is determined by a majority voting procedure.

28. The method of claim 15, wherein the correction factor (CF) for a meal is determined from the raw log data input of the MS Section.

29. The method of claim 27, wherein the correction factor (CF) for the meal is calculated by processing the AIF to estimate the active insulin in the MS Section and a just-in-time carbohydrate ratio (CR).

30. The method of claim 15, wherein a non-meal correction factor (CF) is determined from the raw log data input of a BS Section.

31. The method of claim 15, wherein the correction factor (CF) is modified in accordance with the following parameters:

a proportion between a minimum sensor reading during a time window or section, a lowest blood glucose reading recorded outside impending hypoglycaemia and hypoglycaemia time periods; and

a maximum sensor reading in a time slot prior to obtaining the minimum sensor reading.

32. The method of claim 15, wherein a plurality of candidate correction factors (CF) are determined and the correction factor (CF) setting is determined by a voting procedure.

33. The method of claim 15, wherein said obtaining of the raw log data input is performed by uploading said raw log data input to enable communication of the raw log data input to an unsupervised learning controller.

34. A method for determining an Active Insulin Function (AIF) for use in insulin treatment of a patient, the method comprising:

a) obtaining raw log data obtained over a certain time and being indicative of glucose measurements of the patient,

b) sectioning the raw log data being indicative of glucose measurements, meals events and insulin delivery of the patient to predetermined data sections;

each of the predetermined data sections being at least one of Meals data Section (MS) and Bolus data Section (BS), each section having a starting point and having an end point such that the MS being selected at a predetermined time ahead of a meal data point, and the starting point of the BS being selected as one of the following;

(a) the end point of the MS or the BaS, and (b) an insulin bolus data point which is outside the MS;

the end point of the BS being selected as one of the following, (i) the starting point of the MS or BaS and (ii) a predetermined time ahead of the insulin bolus data point without any intervening insulin bolus;

c) obtaining a set of glucose measurements and paired time stamps for the raw log data in the time section;

d) normalizing each glucose measurement of the set thereby obtaining a series of normalized glucose measurements and paired time stamp;

e) processing said normalized glucose measurements and paired time stamp into a substantially monotonic non-increasing series;

thereby obtaining the Active Insulin Function (AIF).

35. A control unit for use with diabetic treatment management, the control unit comprising:

a data processor utility configured and operable as an unsupervised learning controller preprogrammed for processing raw log data input obtained over a certain time and being indicative of glucose measurements, meals events and insulin delivery, said processing comprising sectioning the raw log data to predetermined data sections;

each of the predetermined data sections being at least one of Basal data Section (BaS), Meals data Section (MS) and Bolus data Section (BS), each section having a starting point and having an end point such that the BaS being selected outside an effect window of either a meal event or an insulin bolus, the MS being selected at a predetermined time ahead of a meal data point, and the starting point of the BS being selected as one of the following;

(a) the end point of the MS or the BaS, and (b) an insulin bolus data point which is outside the MS;

the end point of the BS being selected as one of the following, (i) the starting point of the MS or BaS and (ii) a predetermined time ahead of the insulin bolus data point without any intervening insulin bolus;

determining an informative data piece from residual log data portion of said raw log data and selecting said informative data piece for further processing to determine at least one of basal rate, correction factor (CF), carbohydrate ratio (CR) and insulin activity curve parameters, and generating global insulin pump settings wherein calculation of the basal rate is based on BaS, calculation of at least one of insulin activity curve parameters, correction factor (CF) and carbohydrate ratio (CR) is based on MS, and calculation of the correction factor (CF) or insulin activity curve parameters is based on BS.

36. A computer program recordable on a storage medium and comprising a machine readable format, the computer program being configured and operable to, when being accessed, carry out the following:

identifying raw log data input corresponding to a certain time period and comprising glucose measurements, meals events and insulin delivery;

sectioning the raw log data to predetermined data sections;

each of the predetermined data sections being at least one of Basal data Section (BaS), Meals data Section (MS) and Bolus data Section (BS), each section having a starting point and having an end point such that the BaS being selected outside an effect window of either a meal or an insulin bolus, the MS being selected at a predetermined time ahead of a meal data point, and the starting point of the BS being selected as one of the following;

(a) the end point of the MS or the BaS, and (b) an insulin bolus data point which is outside the MS;

the end point of the BS being selected as one of the following, (i) the starting point of the MS or BaS and (ii) a predetermined time ahead of the insulin bolus data point without any intervening insulin bolus;

determining an informative data piece and residual log data portion of said raw log data;

selecting said informative data piece and calculating therefrom at least one of basal rate, correction factor (CF), carbohydrate ratio (CR) and insulin activity curve parameters, and generating output data comprising values for global insulin pump settings wherein calculation of the basal rate is based on BaS, calculation of at least one of insulin activity curve parameters, correction factor (CF) and carbohydrate ratio (CR) is based on MS, and calculation of the correction factor (CF) or insulin activity curve parameters is based on BS.